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Found 16 papers, 5 papers with code
Symbol Grounding and Task Learning from Imperfect Corrections
This paper describes a method for learning from a teacher’s potentially unreliable corrective feedback in an interactive task learning setting.
Interactive Symbol Grounding with Complex Referential Expressions
We present a procedure for learning to ground symbols from a sequence of stimuli consisting of an arbitrarily complex noun phrase (e. g. “all but one green square above both red circles.”) and its designation in the visual scene.
Dialogue-based generation of self-driving simulation scenarios using Large Language Models
Simulation is an invaluable tool for developing and evaluating controllers for self-driving cars.
Dynamic Planning with a LLM
While Large Language Models (LLMs) can solve many NLP tasks in zero-shot settings, applications involving embodied agents remain problematic.
Interactive Acquisition of Fine-grained Visual Concepts by Exploiting Semantics of Generic Characterizations in Discourse
Interactive Task Learning (ITL) concerns learning about unforeseen domain concepts via natural interactions with human users.
Learning Manner of Execution from Partial Corrections
Some actions must be executed in different ways depending on the context.
Learning the Effects of Physical Actions in a Multi-modal Environment
However, predicting the effects of an action before it is executed is crucial in planning, where coherent sequences of actions are often needed to achieve a goal.
Disentangled Relational Representations for Explaining and Learning from Demonstration
Learning from demonstration is an effective method for human users to instruct desired robot behaviour.
Learning Factored Markov Decision Processes with Unawareness
Methods for learning and planning in sequential decision problems often assume the learner is aware of all possible states and actions in advance.
Interpretable Latent Spaces for Learning from Demonstration
Effective human-robot interaction, such as in robot learning from human demonstration, requires the learning agent to be able to ground abstract concepts (such as those contained within instructions) in a corresponding high-dimensional sensory input stream from the world.
Reasoning about Unforeseen Possibilities During Policy Learning
Methods for learning optimal policies in autonomous agents often assume that the way the domain is conceptualised---its possible states and actions and their causal structure---is known in advance and does not change during learning.
Grounding Symbols in Multi-Modal Instructions
As robots begin to cohabit with humans in semi-structured environments, the need arises to understand instructions involving rich variability---for instance, learning to ground symbols in the physical world.
Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents
In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game {``}Settlers of Catan{''}.
